Learning at the speed of light: A new type of optical neural network
Abstract
Most, if not all, optical hardware-based neural networks are slow during the neural learning phase. This limitation has been not only a speed bottleneck, but it has contributed to the lack of wide-spread use of optical neural systems. We present a novel solution - Optical Fixed-Weight Learning Neural Networks. Standard neural networks learn new function mappings by the changing of their synaptic weights. However, the Fixed-Weight Neural Networks learn new mappings by dynamically changing recurrent neural signals. The (fixed) synaptic weights of the FWL-NN implement a learning "algorithm" which adjusts the recurrent signals toward their proper values.
Department(s)
Physics, Astronomy, and Materials Science
Document Type
Conference Proceeding
DOI
https://doi.org/10.1007/978-3-540-85673-3_9
Keywords
Adaptive Neural Networks, Fixed-Weight Learning Neural Networks, Optical Computing, Optical Neural Networks
Publication Date
12-1-2008
Recommended Citation
Younger, Arthur Steven, and Emmett Redd. "Learning at the speed of light: A new type of optical neural network." In International Workshop on Optical Supercomputing, pp. 104-114. Springer, Berlin, Heidelberg, 2008.
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
